Machine Learning Engineer, Model Quantization, Tesla AI

What You'll Do

• Architect and scale quantization pipelines (both Post-Training Quantization and Quantization-Aware Training) for massive multi-modal foundation models that fuse vision, prediction, and decision-making.
• Optimize inference latency, memory bandwidth utilization, and power consumption for self-driving cars, Optimus robots, and digital agents operating at enterprise scale.
• Innovate quantization-aware-training recipes and algorithms that tackle complex optimization challenges inherent to low-precision training.
• Push the limits of low-precision AI: Research and implement advanced low-bitweight post-training quantization techniques to address hard algorithmic problems such as activation outlier mitigation, KV cache compression, and optimal layer-wise bit-allocation while strictly maintaining model accuracy.
• Collaborate closely with AI compiler, inference engine, and silicon teams to ensure models are architected to maximize underlying hardware capabilities by co-designing quantization-friendly architectures, hardware-aware sparsity patterns, and mixed low-precision kernels.
• Collaborate across perception, planning, robotics, digital agents, and infrastructure teams to move models from research to fleet-wide, robot-wide, and enterprise-wide deployment.

What You'll Bring

• Degree or equivalent experience in Computer Science, Machine Learning, Robotics, Computer Vision, or related quantitative field
• 2+ years of hands-on experience training, optimizing, and deploying large-scale quantized deep learning models
• Strong technical understanding of the challenges inherent to quantizing large transformer architectures, including mitigating massive activation outliers, KV cache quantization, and maintaining the numerical stability of attention mechanisms at low precision
• Deep expertise in the theory and low-level implementation of modern quantization algorithms (e.g., GPTQ, AWQ, SmoothQuant, OmniQuant)
• Experience with low-level numerics and emerging data formats (e.g., FP8, INT4, W4A8, W8A8, micro-scaling/MX formats) and their trade-offs regarding latency, memory bandwidth, and model fidelity
• Rigorous understanding of computer architecture and the roofline model. Familiarity with how to optimize for memory hierarchies, minimize SRAM/DRAM data movement, and efficiently map quantized GEMMs and memory-bound operators to custom silicon
• Proficiency in writing custom CUDA/Triton kernels, implementing custom autograd functions (e.g., Straight-Through Estimators), and manipulating PyTorch computational graphs (e.g., FX tracing, torch.compile)
• Strong software engineering skills — clean, production-grade Python/C++ code that ships reliably at scale
• Proven ability to turn cutting-edge research into robust, real-world systems that improve safety, capability, efficiency, or digital productivity
• Passion for Tesla’s mission and excitement about deploying AI that moves both the physical and digital worlds forward

Benefits

• Competitive pay
• Medical plans
• Fertility, adoption and surrogacy benefits
• Dental and vision plans
• Healthcare and Dependent Care Flexible Spending Accounts (FSA)
• Employee Stock Purchase Plans
• Other financial benefits
• Basic Life, AD&D Short-term and long-term disability insurance
• Employee Assistance Program
• Sick and Vacation time
• Paid Holidays
• Back-up childcare and parenting support resources
• Voluntary benefits to include: critical illness, hospital indemnity, accident insurance, theft & legal services, and pet insurance
• Weight Loss and Tobacco Cessation Programs
• Expected Compensation: $124,000 - $558,000/annual salary + cash and stock awards + benefits